As a technique of obtaining a time-series data that changes as time lapses/progresses from a detection signal of a sensor and analyzing data, a technique of analyzing a fuel injection data is known, i.e., obtaining a time series data regarding fuel injection from a fuel injection valve of an internal combustion engine based on a detection signal of a sensor for an analysis, for example (see a patent document 1 listed below).
In the technique disclosed by a patent document, JP4130823B (patent document 1), the change of the fuel pressure regarding a fuel injected by the fuel injection valve along time, for example, is detected by the pressure sensor as the time-series data of the fuel injection which changes along time, i.e., as time lapses. Then, the detected fuel pressure is used for the measurement of the injection rate.
When obtaining the time-series data that changes along time from the detection signal of the sensor and analyzing them, it is possible to detect a characteristic point as the characteristic of the time-series data. In terms of the time-series data regarding the fuel injection, an injection start timing, an injection end timing, an ignition timing and the like may be, for example, picked up as the characteristic point.
Since the detection signal of the sensor includes noise, the time-series data obtained from the detection signal of the sensor is ridden by the noise. Therefore, even by the comparison between (i) a determination value for detecting the injection start timing, the injection end timing, the ignition timing, etc., and (ii) the time-series data, it may be difficult to detect the characteristic point.
In view of the above situation, a filter such as a low-pass filter may be used for removing the noise from the time-series data. However, when the filter is used, the waveform of time-series data becomes blunt, the timing of the characteristic point shifts and a mis-detection of the characteristic point is caused. When the mis-detection of the characteristic point of the time-series data is caused, the characteristic of the time-series data is not appropriately analyzable.